# Fine Tuning a Simulation-Driven Estimator

arXiv: https://arxiv.org/abs/2504.04480 · HTML: https://mlsanity.com/paper/2504.04480/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Robust Neural Posterior Estimation and Statistical Model Criticism](https://mlsanity.com/paper/2210.06564/) (2022)
- [Truncated proposals for scalable and hassle-free simulation-based inference](https://mlsanity.com/paper/2210.04815/) (2022)
- [A survey and taxonomy of loss functions in machine learning](https://mlsanity.com/paper/2301.05579/) (2023)
- [On the adaptation of recurrent neural networks for system identification](https://mlsanity.com/paper/2201.08660/) (2022)
- [Learning to Estimate Without Bias](https://mlsanity.com/paper/2110.12403/) (2021)
- [Neural Posterior Estimation with Differentiable Simulators](https://mlsanity.com/paper/2207.05636/) (2022)
- [Synthetic data generation for system identification: leveraging knowledge transfer from similar systems](https://mlsanity.com/paper/2403.05164/) (2024)
- [Deep transfer learning for system identification using long short-term memory neural networks](https://mlsanity.com/paper/2204.03125/) (2022)
- [DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models](https://mlsanity.com/paper/2205.02264/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach](https://mlsanity.com/paper/2606.24966/) (2026)

## Abstract (arXiv)

Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations.